
# Run this code when you restart the machine
# Fill in with YOUR name and NIM
import datetime
import uuid

myName = "Christopher Darren"
myNIM = "00000054804"


myDate = datetime.datetime.now()
myDevice = str(uuid.uuid1())

print("Name: \t\t{}".format(myName))
print("NIM: \t\t{}".format(myNIM))
print("Start: \t\t{}".format(myDate))
print("Device ID: \t{}".format(myDevice))

Name: 		Christopher Darren
NIM: 		00000054804
Start: 		2023-05-04 08:08:58.986644
Device ID: 	3fdc3dcd-ea18-11ed-87cb-f02f74a116e8


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


df = pd.read_csv(r"D:\SEMESTER 4\IF540 Machine Learning\LAB\week10\all_seasons.csv")


df.shape

(12305, 22)


df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12305 entries, 0 to 12304
Data columns (total 22 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Unnamed: 0         12305 non-null  int64  
 1   player_name        12305 non-null  object 
 2   team_abbreviation  12305 non-null  object 
 3   age                12305 non-null  float64
 4   player_height      12305 non-null  float64
 5   player_weight      12305 non-null  float64
 6   college            12305 non-null  object 
 7   country            12305 non-null  object 
 8   draft_year         12305 non-null  object 
 9   draft_round        12305 non-null  object 
 10  draft_number       12305 non-null  object 
 11  gp                 12305 non-null  int64  
 12  pts                12305 non-null  float64
 13  reb                12305 non-null  float64
 14  ast                12305 non-null  float64
 15  net_rating         12305 non-null  float64
 16  oreb_pct           12305 non-null  float64
 17  dreb_pct           12305 non-null  float64
 18  usg_pct            12305 non-null  float64
 19  ts_pct             12305 non-null  float64
 20  ast_pct            12305 non-null  float64
 21  season             12305 non-null  object 
dtypes: float64(12), int64(2), object(8)
memory usage: 2.1+ MB


df.describe()


df.isnull().sum()

Unnamed: 0           0
player_name          0
team_abbreviation    0
age                  0
player_height        0
player_weight        0
college              0
country              0
draft_year           0
draft_round          0
draft_number         0
gp                   0
pts                  0
reb                  0
ast                  0
net_rating           0
oreb_pct             0
dreb_pct             0
usg_pct              0
ts_pct               0
ast_pct              0
season               0
dtype: int64


#numerical = ['age','player_height','player_weight','draft_number','gp','pts','reb','ast','net_rating','oreb_pct','dreb_pct',
#             'usg_pct','ts_pct','ast_pct']
numerical = ['age','player_height','player_weight','pts']
categorical = ['team_abbreviation','country','draft_year','draft_round']
unused = ['Unnamed: 0','player_name','college','season']


df = df.drop(columns = unused)
df.shape

(12305, 18)


for cat in categorical:
    df[cat] = df[cat].fillna(df[cat].mode().values[0])


df.isnull().sum()

team_abbreviation    0
age                  0
player_height        0
player_weight        0
country              0
draft_year           0
draft_round          0
draft_number         0
gp                   0
pts                  0
reb                  0
ast                  0
net_rating           0
oreb_pct             0
dreb_pct             0
usg_pct              0
ts_pct               0
ast_pct              0
dtype: int64


sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x18da9957640>


fig = plt.figure(figsize = (25,25))
axes = 320
for cat in categorical:
    axes += 1
    fig.add_subplot(axes)
    sns.countplot(data = df, x =cat)
    plt.xticks(rotation=30)
plt.show()


#create correlation
corr = df.corr(method ='pearson')

#convert correlation to numpy array
mask = np.array(corr)

#to mask the repetitive value for each pair
mask[np.tril_indices_from(mask)] = False
fig, ax = plt.subplots(figsize = (15, 12))
fig.set_size_inches(15,15)
sns.heatmap(corr, mask = mask, vmax = 0.9, square = True, annot = True)

<AxesSubplot:>


df_cluster = pd.DataFrame()
#df_cluster['usg_pct'] = df['usg_pct']
df_cluster['age'] = df['age']
#df_cluster['ast_pct'] = df['ast_pct']
df_cluster['player_height'] = df['player_height']
df_cluster['player_weight'] = df['player_weight']
#df_cluster['gp'] = df['gp']
df_cluster['pts'] = df['pts']
#df_cluster['reb'] = df['reb']
#df_cluster['ast'] = df['ast']
#df_cluster['net_rating'] = df['net_rating']
#df_cluster['oreb_pct'] = df['oreb_pct']
#df_cluster['dreb_pct'] = df['dreb_pct']
#df_cluster['ts_pct'] = df['ts_pct']
#df_cluster['draft_round'] = df['draft_round']
df_cluster.head()


fig = plt.figure(figsize = (15,10))
axes = 220
for num in numerical:
    axes += 1
    fig.add_subplot(axes)
    sns.boxplot(data = df, x= num)
plt.show()


fig, (ax1, ax2, ax3, ax4) = plt.subplots(4,1, figsize=(8,8))
sns.distplot(df["age"], ax=ax1)
sns.distplot(df["player_height"], ax=ax2)
sns.distplot(df["player_weight"], ax=ax3)
sns.distplot(df["pts"], ax=ax4)
plt.tight_layout()
plt.legend()

C:\Users\Darren\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Darren\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Darren\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Darren\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

<matplotlib.legend.Legend at 0x18db79a9bb0>


df_cluster_log = np.log(df_cluster[['age','player_height','player_weight']])

fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8,8))
sns.distplot(df_cluster_log["age"], ax=ax1)
sns.distplot(df_cluster_log["player_height"], ax=ax2)
sns.distplot(df_cluster_log["player_weight"], ax=ax3)
plt.tight_layout()

C:\Users\Darren\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Darren\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Darren\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


df_cluster_log.head()


from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
cluster_scaled = scaler.fit_transform(df_cluster_log)


import scipy.cluster.hierarchy as sch

plt.figure(figsize=(20,10))
dendrogram = sch.dendrogram(sch.linkage(cluster_scaled, method='ward'))


from sklearn.cluster import AgglomerativeClustering
from mpl_toolkits.mplot3d import Axes3D
model = AgglomerativeClustering(n_clusters=3)
model.fit(cluster_scaled)
hac_labels = model.labels_

fig = plt.figure(num=None, figsize=(15,20), dpi=80, facecolor='w', edgecolor='k')
ax = plt.axes(projection="3d")

ax.scatter3D(df_cluster['age'],df_cluster['player_height'],df_cluster['player_weight'],c=hac_labels,cmap='rainbow')

xLabel = ax.set_xlabel('age', linespacing=3.2)
yLabel = ax.set_ylabel('player_height', linespacing=3.1)
zLabel = ax.set_zlabel('player_weight', linespacing=3.4)
print("Hierarchical Agglomerative Clustering")

Hierarchical Agglomerative Clustering


df_clustered_hac = df_cluster.assign(Cluster=hac_labels)
grouped_hac = df_clustered_hac.groupby(['Cluster']).mean().round(1)
grouped_hac


#The Dendrogram using breastcancer dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


data = pd.read_csv(r'D:\SEMESTER 4\IF540 Machine Learning\LAB\week10\data.csv')
data.head()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 33 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   id                       569 non-null    int64  
 1   diagnosis                569 non-null    object 
 2   radius_mean              569 non-null    float64
 3   texture_mean             569 non-null    float64
 4   perimeter_mean           569 non-null    float64
 5   area_mean                569 non-null    float64
 6   smoothness_mean          569 non-null    float64
 7   compactness_mean         569 non-null    float64
 8   concavity_mean           569 non-null    float64
 9   concave points_mean      569 non-null    float64
 10  symmetry_mean            569 non-null    float64
 11  fractal_dimension_mean   569 non-null    float64
 12  radius_se                569 non-null    float64
 13  texture_se               569 non-null    float64
 14  perimeter_se             569 non-null    float64
 15  area_se                  569 non-null    float64
 16  smoothness_se            569 non-null    float64
 17  compactness_se           569 non-null    float64
 18  concavity_se             569 non-null    float64
 19  concave points_se        569 non-null    float64
 20  symmetry_se              569 non-null    float64
 21  fractal_dimension_se     569 non-null    float64
 22  radius_worst             569 non-null    float64
 23  texture_worst            569 non-null    float64
 24  perimeter_worst          569 non-null    float64
 25  area_worst               569 non-null    float64
 26  smoothness_worst         569 non-null    float64
 27  compactness_worst        569 non-null    float64
 28  concavity_worst          569 non-null    float64
 29  concave points_worst     569 non-null    float64
 30  symmetry_worst           569 non-null    float64
 31  fractal_dimension_worst  569 non-null    float64
 32  Unnamed: 32              0 non-null      float64
dtypes: float64(31), int64(1), object(1)
memory usage: 146.8+ KB


data.shape

(569, 33)


#encoding

def encode_data(feature_name):
    '''
    This function takes feature name as a parameter and returns mapping dictionary to replace(or map) categorical data with
    '''
    mapping_dict = {}
    
    unique_values = list(dataLogistic[feature_name].unique())
    
    
    for idx in range(len(unique_values)):
        
        mapping_dict[unique_values[idx]] = idx
        
    return mapping_dict


data['diagnosis'].replace({'M':0,'B': 1}, inplace = True)


data.isnull().sum()

id                           0
diagnosis                    0
radius_mean                  0
texture_mean                 0
perimeter_mean               0
area_mean                    0
smoothness_mean              0
compactness_mean             0
concavity_mean               0
concave points_mean          0
symmetry_mean                0
fractal_dimension_mean       0
radius_se                    0
texture_se                   0
perimeter_se                 0
area_se                      0
smoothness_se                0
compactness_se               0
concavity_se                 0
concave points_se            0
symmetry_se                  0
fractal_dimension_se         0
radius_worst                 0
texture_worst                0
perimeter_worst              0
area_worst                   0
smoothness_worst             0
compactness_worst            0
concavity_worst              0
concave points_worst         0
symmetry_worst               0
fractal_dimension_worst      0
Unnamed: 32                569
dtype: int64


cleandata = data.drop(['Unnamed: 32','id'], axis=1)
cleandata


from sklearn.preprocessing import normalize
data_scaled=normalize(cleandata)
data_scaled=pd.DataFrame(data_scaled, columns=cleandata.columns)
data_scaled.head()


import scipy.cluster.hierarchy as shc
plt.figure(figsize=(10,7))
plt.title("dendrograms")
dend=shc.dendrogram(shc.linkage(data_scaled,method='ward'))


plt.figure(figsize=(10,7))
plt.title("Dendrograms")
dend=shc.dendrogram(shc.linkage(data_scaled,method='ward'))
plt.axhline(y=1,color='r', linestyle='--')
#plt.ylim(bottom=0, top=18) # adjust the y-axis limits
plt.show()


from sklearn.cluster import AgglomerativeClustering
cluster=AgglomerativeClustering(n_clusters=2, affinity='euclidean',
                               linkage='ward')
cluster.fit_predict(data_scaled)

array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,
       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,
       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0,
       1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0,
       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,
       1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,
       1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,
       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
       0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0,
       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,
       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,
       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,
       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1],
      dtype=int64)


plt.figure(figsize=(10,7))
plt.scatter(data_scaled['texture_worst'], data_scaled['area_worst'], c=cluster.labels_)

<matplotlib.collections.PathCollection at 0x18dcaecec40>


# Footer
myDate = datetime.datetime.now()
print("I certify that this is my own work.")
print("Signed by:")
print("Name: \t\t{}".format(myName))
print("NIM: \t\t{}".format(myNIM))
print("Time-stamp:\t{}".format(myDate))

I certify that this is my own work.
Signed by:
Name: 		Christopher Darren
NIM: 		00000054804
Time-stamp:	2023-05-04 19:55:07.329487


!jupyter nbconvert --to html "./IF540_Kelas_EL_00000054804_Christopher Darren_Week10.ipynb" --output-dir="./"

This application is used to convert notebook files (*.ipynb)
        to various other formats.

        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.

Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
    <cmd> --help-all

--debug
    set log level to logging.DEBUG (maximize logging output)
    Equivalent to: [--Application.log_level=10]
--show-config
    Show the application's configuration (human-readable format)
    Equivalent to: [--Application.show_config=True]
--show-config-json
    Show the application's configuration (json format)
    Equivalent to: [--Application.show_config_json=True]
--generate-config
    generate default config file
    Equivalent to: [--JupyterApp.generate_config=True]
-y
    Answer yes to any questions instead of prompting.
    Equivalent to: [--JupyterApp.answer_yes=True]
--execute
    Execute the notebook prior to export.
    Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
    Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
    Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
    Write notebook output to stdout instead of files.
    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
    Run nbconvert in place, overwriting the existing notebook (only 
            relevant when converting to notebook format)
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
    Clear output of current file and save in place, 
            overwriting the existing notebook.
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--no-prompt
    Exclude input and output prompts from converted document.
    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
    Exclude input cells and output prompts from converted document. 
            This mode is ideal for generating code-free reports.
    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
    Whether to allow downloading chromium if no suitable version is found on the system.
    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
    Disable chromium security sandbox when converting to PDF..
    Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
    Shows code input. This flag is only useful for dejavu users.
    Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
    Equivalent to: [--HTMLExporter.embed_images=True]
--log-level=<Enum>
    Set the log level by value or name.
    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
    Default: 30
    Equivalent to: [--Application.log_level]
--config=<Unicode>
    Full path of a config file.
    Default: ''
    Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
    The export format to be used, either one of the built-in formats
            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf']
            or a dotted object name that represents the import path for an
            ``Exporter`` class
    Default: ''
    Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
    Name of the template to use
    Default: ''
    Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
    Name of the template file to use
    Default: None
    Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
    as prebuilt extension for the lab template)
    Default: 'light'
    Equivalent to: [--HTMLExporter.theme]
--writer=<DottedObjectName>
    Writer class used to write the 
                                        results of the conversion
    Default: 'FilesWriter'
    Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>

[NbConvertApp] WARNING | pattern './IF540_Kelas_EL_00000054804_Christopher Darren_Week10.ipynb' matched no files

    PostProcessor class used to write the
                                        results of the conversion
    Default: ''
    Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
    overwrite base name use for output files.
                can only be used when converting one notebook at a time.
    Default: ''
    Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
    Directory to write output(s) to. Defaults
                                  to output to the directory of each notebook. To recover
                                  previous default behaviour (outputting to the current 
                                  working directory) use . as the flag value.
    Default: ''
    Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
    The URL prefix for reveal.js (version 3.x).
            This defaults to the reveal CDN, but can be any url pointing to a copy 
            of reveal.js. 
            For speaker notes to work, this must be a relative path to a local 
            copy of reveal.js: e.g., "reveal.js".
            If a relative path is given, it must be a subdirectory of the
            current directory (from which the server is run).
            See the usage documentation
            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
            for more details.
    Default: ''
    Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
    The nbformat version to write.
            Use this to downgrade notebooks.
    Choices: any of [1, 2, 3, 4]
    Default: 4
    Equivalent to: [--NotebookExporter.nbformat_version]

Examples
--------

    The simplest way to use nbconvert is

            > jupyter nbconvert mynotebook.ipynb --to html

            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].

            > jupyter nbconvert --to latex mynotebook.ipynb

            Both HTML and LaTeX support multiple output templates. LaTeX includes
            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and 
            'classic'. You can specify the flavor of the format used.

            > jupyter nbconvert --to html --template lab mynotebook.ipynb

            You can also pipe the output to stdout, rather than a file

            > jupyter nbconvert mynotebook.ipynb --stdout

            PDF is generated via latex

            > jupyter nbconvert mynotebook.ipynb --to pdf

            You can get (and serve) a Reveal.js-powered slideshow

            > jupyter nbconvert myslides.ipynb --to slides --post serve

            Multiple notebooks can be given at the command line in a couple of 
            different ways:

            > jupyter nbconvert notebook*.ipynb
            > jupyter nbconvert notebook1.ipynb notebook2.ipynb

            or you can specify the notebooks list in a config file, containing::

                c.NbConvertApp.notebooks = ["my_notebook.ipynb"]

            > jupyter nbconvert --config mycfg.py

To see all available configurables, use `--help-all`.

	Unnamed: 0	player_name	team_abbreviation	age	player_height	player_weight	college	country	draft_year	draft_round	...	pts	reb	ast	net_rating	oreb_pct	dreb_pct	usg_pct	ts_pct	ast_pct	season
0	0	Dennis Rodman	CHI	36.0	198.12	99.790240	Southeastern Oklahoma State	USA	1986	2	...	5.7	16.1	3.1	16.1	0.186	0.323	0.100	0.479	0.113	1996-97
1	1	Dwayne Schintzius	LAC	28.0	215.90	117.933920	Florida	USA	1990	1	...	2.3	1.5	0.3	12.3	0.078	0.151	0.175	0.430	0.048	1996-97
2	2	Earl Cureton	TOR	39.0	205.74	95.254320	Detroit Mercy	USA	1979	3	...	0.8	1.0	0.4	-2.1	0.105	0.102	0.103	0.376	0.148	1996-97
3	3	Ed O'Bannon	DAL	24.0	203.20	100.697424	UCLA	USA	1995	1	...	3.7	2.3	0.6	-8.7	0.060	0.149	0.167	0.399	0.077	1996-97
4	4	Ed Pinckney	MIA	34.0	205.74	108.862080	Villanova	USA	1985	1	...	2.4	2.4	0.2	-11.2	0.109	0.179	0.127	0.611	0.040	1996-97

	Unnamed: 0	age	player_height	player_weight	gp	pts	reb	ast	net_rating	oreb_pct	dreb_pct	usg_pct	ts_pct	ast_pct
count	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000	12305.000000
mean	6152.000000	27.084518	200.611602	100.369926	51.290532	8.172775	3.559155	1.813986	-2.255733	0.054473	0.141014	0.184891	0.511060	0.131358
std	3552.291866	4.335868	9.146321	12.477150	25.095909	5.974957	2.483550	1.794155	12.673254	0.043599	0.062704	0.053390	0.101163	0.094483
min	0.000000	18.000000	160.020000	60.327736	1.000000	0.000000	0.000000	0.000000	-250.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	3076.000000	24.000000	193.040000	90.718400	31.000000	3.600000	1.800000	0.600000	-6.400000	0.021000	0.096000	0.149000	0.480000	0.066000
50%	6152.000000	26.000000	200.660000	99.790240	57.000000	6.700000	3.000000	1.200000	-1.300000	0.041000	0.131000	0.181000	0.524000	0.103000
75%	9228.000000	30.000000	208.280000	108.862080	73.000000	11.500000	4.700000	2.400000	3.200000	0.084000	0.180000	0.217000	0.561000	0.178000
max	12304.000000	44.000000	231.140000	163.293120	85.000000	36.100000	16.300000	11.700000	300.000000	1.000000	1.000000	1.000000	1.500000	1.000000

	age	player_height	player_weight	pts
0	36.0	198.12	99.790240	5.7
1	28.0	215.90	117.933920	2.3
2	39.0	205.74	95.254320	0.8
3	24.0	203.20	100.697424	3.7
4	34.0	205.74	108.862080	2.4

	age	player_height	player_weight
0	3.583519	5.288873	4.603070
1	3.332205	5.374815	4.770124
2	3.663562	5.326613	4.556550
3	3.178054	5.314191	4.612120
4	3.526361	5.326613	4.690082

	age	player_height	player_weight	pts
Cluster
0	25.9	192.2	89.7	8.4
1	30.6	205.4	107.1	8.3
2	23.3	208.2	108.9	7.5

TUGAS LAB IF540 MACHINE LEARNING
¶

WEEK 10 : Algoritma Hierarki
¶

Semester Genap 2022/2023
¶

Dataset yang dipakai:¶

Deskripsi Dataset:¶

1. NBA Basket dataset merupakan sebuah dataset yang berisi statistik demografi pemain nba, dimulai dari umur, tinggi badan, berat badan, tempat kelahiran, dan detail lainnya. Dataset ini dari (12305 rows , 22 columns)¶

2. Breast Cancer dataset merupakan sebuah dataset yang berisi statistik mengenai karakteristik kanker payudara. Dataset ini terdapat (569 rows, 33 columns).¶

Hasil kerja¶

Hierarchical Clustering : Agglomerative Clustering¶

1. Import the important libraries and read the dataset (‘all_seasons.csv’)¶

2. Take a quick look the dataset :¶

3. Missing values identification handling¶

4. Let’s visualize the dataset¶

Above are the bar plot of all categorical feature. From the bar plot above, we can get some insight. That are :¶

1. Team abbreviation dengan team terbanyak adalah team CLE¶

2. Player dengan kewarganegaraan terbanyak terdapat di negara US¶

3. Draft round terbanyak ada di round 1 dengan jumlah 7000-an¶

4. Jumlah draft year terbanyak ada di kategori unlisted/ tidak terdaftar(Undrafted) dengan jumlah 2500-an lebih.¶

5. From the heatmap above we can see that the best correlation is between age and player height. But so far we will still use all the numeric features for the clustering.¶

From the figure above, we can see that distributions are right-skewed and kinda left skewed.¶

Feature Engineering¶

6. We can use logarithmic transformation to reduce the outliers and distribution skewness.¶

7. We can see that the skewness of the distributions is eliminated. Beside log transformation, we can use Fit and Transform.¶

8. Preprocessing the dataset using StandardScaler()¶

9. Models the dataset using Hierarchical Agglomerative Clustering.¶

10. From the dendrogram above, we can see that the most optimal n_clusters is 3¶

Cluster 0 : Umur muda, cukup tinggi, berat badan paling ringan, point paling tinggi¶

Cluster 1 : Umur tertua, tinggi, berat badan berat, point kedua tertinggi setelah cluster 0¶

Cluster 2 : Umur paling muda, paling tinggi,berat badan kedua paling berat setelah cluster 1, points paling kecil¶

The Dendrogram¶

11. Import the required libraries :¶

12. Look the data and look at the first few rows¶

13. Let’s normalize the data and bring all the variables to the same scale:¶

14. Here we can see that the scale of all the variables is almost similar. Now, we are good to go. Let’s first draw the dendrogram to help us decide the number of clusters for this particular problem :¶

15. The x-axis contains the samples and y-axis represents the distance between these samples. The vertical line with maximum distance is the blue line and hence we can decide a threshold of 1 and cut the dendrogram :¶

16. We have 2 clusters as this line cuts the dendrogram at two points. Let’s now apply hierarchical clustering for 2 clusters :¶

We can see the values of 0s and 1s in the output since we defined 2 clusters. 0 represents the points that belong to the first cluster and 1 represents points in the second cluster.¶

17. Lets now visualize the two clusters :¶

Kesimpulan¶

Save the notebook, then convert the notebook to html (by running the next code).¶

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	...	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890	NaN
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	...	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902	NaN
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	...	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758	NaN
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	...	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300	NaN
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	...	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678	NaN

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	0.007925	0.004573	0.054099	0.440986	0.000052	0.000122	0.000132	0.000065	0.000107	...	0.011181	0.007635	0.081325	0.889462	0.000071	0.000293	0.000314	0.000117	0.000203	0.000052
1	0.008666	0.007486	0.055988	0.558619	0.000036	0.000033	0.000037	0.000030	0.000076	...	0.010528	0.009862	0.066899	0.824026	0.000052	0.000079	0.000102	0.000078	0.000116	0.000038
2	0.009367	0.010109	0.061842	0.572276	0.000052	0.000076	0.000094	0.000061	0.000098	...	0.011212	0.012145	0.072545	0.812984	0.000069	0.000202	0.000214	0.000116	0.000172	0.000042
3	0.016325	0.029133	0.110899	0.551922	0.000204	0.000406	0.000345	0.000150	0.000371	...	0.021314	0.037881	0.141333	0.811515	0.000300	0.001238	0.000982	0.000368	0.000949	0.000247
4	0.009883	0.006985	0.065808	0.631774	0.000049	0.000065	0.000096	0.000051	0.000088	...	0.010979	0.008120	0.074137	0.767189	0.000067	0.000100	0.000195	0.000079	0.000115	0.000037

TUGAS LAB IF540 MACHINE LEARNING ¶

WEEK 10 : Algoritma Hierarki ¶

Semester Genap 2022/2023 ¶

Dataset yang dipakai:¶

Deskripsi Dataset:¶

1. NBA Basket dataset merupakan sebuah dataset yang berisi statistik demografi pemain nba, dimulai dari umur, tinggi badan, berat badan, tempat kelahiran, dan detail lainnya. Dataset ini dari (12305 rows , 22 columns)¶

2. Breast Cancer dataset merupakan sebuah dataset yang berisi statistik mengenai karakteristik kanker payudara. Dataset ini terdapat (569 rows, 33 columns).¶

Hasil kerja¶

Hierarchical Clustering : Agglomerative Clustering¶

1. Import the important libraries and read the dataset (‘all_seasons.csv’)¶

2. Take a quick look the dataset :¶

3. Missing values identification handling¶

4. Let’s visualize the dataset¶

Above are the bar plot of all categorical feature. From the bar plot above, we can get some insight. That are :¶

1. Team abbreviation dengan team terbanyak adalah team CLE¶

2. Player dengan kewarganegaraan terbanyak terdapat di negara US¶

3. Draft round terbanyak ada di round 1 dengan jumlah 7000-an¶

4. Jumlah draft year terbanyak ada di kategori unlisted/ tidak terdaftar(Undrafted) dengan jumlah 2500-an lebih.¶

5. From the heatmap above we can see that the best correlation is between age and player height. But so far we will still use all the numeric features for the clustering.¶

From the figure above, we can see that distributions are right-skewed and kinda left skewed.¶

Feature Engineering¶

6. We can use logarithmic transformation to reduce the outliers and distribution skewness.¶

7. We can see that the skewness of the distributions is eliminated. Beside log transformation, we can use Fit and Transform.¶

8. Preprocessing the dataset using StandardScaler()¶

9. Models the dataset using Hierarchical Agglomerative Clustering.¶

10. From the dendrogram above, we can see that the most optimal n_clusters is 3¶

Cluster 0 : Umur muda, cukup tinggi, berat badan paling ringan, point paling tinggi¶

Cluster 1 : Umur tertua, tinggi, berat badan berat, point kedua tertinggi setelah cluster 0¶

Cluster 2 : Umur paling muda, paling tinggi,berat badan kedua paling berat setelah cluster 1, points paling kecil¶

The Dendrogram¶

11. Import the required libraries :¶

12. Look the data and look at the first few rows¶

13. Let’s normalize the data and bring all the variables to the same scale:¶

14. Here we can see that the scale of all the variables is almost similar. Now, we are good to go. Let’s first draw the dendrogram to help us decide the number of clusters for this particular problem :¶

15. The x-axis contains the samples and y-axis represents the distance between these samples. The vertical line with maximum distance is the blue line and hence we can decide a threshold of 1 and cut the dendrogram :¶

16. We have 2 clusters as this line cuts the dendrogram at two points. Let’s now apply hierarchical clustering for 2 clusters :¶

We can see the values of 0s and 1s in the output since we defined 2 clusters. 0 represents the points that belong to the first cluster and 1 represents points in the second cluster.¶

17. Lets now visualize the two clusters :¶

Kesimpulan¶

Save the notebook, then convert the notebook to html (by running the next code).¶

TUGAS LAB IF540 MACHINE LEARNING
¶

WEEK 10 : Algoritma Hierarki
¶

Semester Genap 2022/2023
¶